Before shipping, products are each measured for a characteristic value representing a predetermined characteristic, and are sorted into non-defective and defective products depending on whether or not a predetermined standard is satisfied. Each product is sorted by comparison of a measured characteristic value of the product with an inspection standard of which conditions are more rigorous than conditions of a product standard (a characteristic value required for the product). If the variation in the measured characteristic values of the products is only the variation in the characteristic values of the products themselves, the products can be correctly sorted into non-defective and defective products even if the conditions of the inspection standard are defined to be the same as those of the product standard.
The variation in the measured characteristic values of the products is, however, not only the variation in the characteristic values of the products themselves, but also includes the variation in values measured by a measuring system. Therefore, products sorted as non-defective products may include defective products, while products sorted as defective products may include non-defective products.
Further, it is difficult to screen all products, and accordingly, only a certain number of products are usually sampled for inspection. In such sampling inspection, the cost for the inspection is reduced as the average number of products to be inspected is smaller.
One known method for doing so is set forth in “Design of Sampling Inspection Plans with Screening by Variables: Sampling Inspection Plans with Screening for Normal Distribution,” Communication of JIMA, Vol. 42, No. 6 (1992), p. 397-405. In this method it is assumed that a quality characteristic is distributed according to a “normal distribution” to assure that a lot acceptance probability in a lot tolerance defect rate is a specified value or less, this publication discloses a method for significantly reducing an average inspection quantity as compared with a sampling inspection plan designated in the product standard.
The method disclosed in this publication presupposes that the product quality characteristic is distributed according to a “normal distribution,” and thus, the accuracy of the OC curve is enhanced by utilizing the characteristic of the “normal distribution.” The method is basically identical to a conventional method for estimating an OC curve, which does not specifically estimate the distribution of product lots. Therefore, there arises a problem that the sufficient estimation accuracy is difficult to be assured.
In view of the foregoing, the present invention aims to provide a sampling data processing device, a sampling data processing method and a computer program for estimating with high accuracy the number of product lots outside the product standard based on data of samples taken from the product lots, by concretely estimating a distribution of the product lots using data of a control chart.